Overview

Dataset statistics

Number of variables21
Number of observations67190
Missing cells333415
Missing cells (%)23.6%
Duplicate rows433
Duplicate rows (%)0.6%
Total size in memory10.3 MiB
Average record size in memory161.0 B

Variable types

Numeric10
Categorical10
Boolean1

Alerts

MarcaVehiculo__c has constant value "97.0" Constant
MdeloVehiculo__c has constant value "999.0" Constant
vigencia_dias has constant value "365" Constant
end_vig has constant value "365.0" Constant
Dataset has 433 (0.6%) duplicate rowsDuplicates
CodigoTipoAsegurado__c is highly correlated with n_prod_prevHigh correlation
PuntoVenta__c is highly correlated with NumeroPoliza__c and 3 other fieldsHigh correlation
Producto__c is highly correlated with ClaseVehiculo__c and 3 other fieldsHigh correlation
ClaseVehiculo__c is highly correlated with Producto__c and 3 other fieldsHigh correlation
TipoVehiculo__c is highly correlated with Producto__c and 4 other fieldsHigh correlation
NumeroPoliza__c is highly correlated with PuntoVenta__c and 3 other fieldsHigh correlation
RamoTecnico__c is highly correlated with n_prod_prevHigh correlation
Tipo_poliza_c is highly correlated with TipoVehiculo__cHigh correlation
n_prod_prev is highly correlated with CodigoTipoAsegurado__c and 6 other fieldsHigh correlation
total_siniestros is highly correlated with PuntoVenta__c and 3 other fieldsHigh correlation
total_pagado_smmlv is highly correlated with PuntoVenta__c and 3 other fieldsHigh correlation
anios_ultimo_siniestro is highly correlated with PuntoVenta__c and 2 other fieldsHigh correlation
PuntoVenta__c is highly correlated with NumeroPoliza__c and 1 other fieldsHigh correlation
Producto__c is highly correlated with ClaseVehiculo__c and 5 other fieldsHigh correlation
ClaseVehiculo__c is highly correlated with Producto__c and 3 other fieldsHigh correlation
TipoVehiculo__c is highly correlated with Producto__c and 3 other fieldsHigh correlation
NumeroPoliza__c is highly correlated with PuntoVenta__c and 4 other fieldsHigh correlation
Tipo_poliza_c is highly correlated with NumeroPoliza__cHigh correlation
n_prod_prev is highly correlated with Producto__c and 2 other fieldsHigh correlation
total_siniestros is highly correlated with PuntoVenta__c and 2 other fieldsHigh correlation
total_pagado_smmlv is highly correlated with Producto__c and 1 other fieldsHigh correlation
CodigoTipoAsegurado__c is highly correlated with n_prod_prevHigh correlation
Producto__c is highly correlated with ClaseVehiculo__c and 2 other fieldsHigh correlation
ClaseVehiculo__c is highly correlated with Producto__c and 3 other fieldsHigh correlation
TipoVehiculo__c is highly correlated with Producto__c and 4 other fieldsHigh correlation
NumeroPoliza__c is highly correlated with ClaseVehiculo__c and 1 other fieldsHigh correlation
RamoTecnico__c is highly correlated with n_prod_prevHigh correlation
Tipo_poliza_c is highly correlated with TipoVehiculo__cHigh correlation
n_prod_prev is highly correlated with CodigoTipoAsegurado__c and 4 other fieldsHigh correlation
total_siniestros is highly correlated with total_pagado_smmlv and 1 other fieldsHigh correlation
total_pagado_smmlv is highly correlated with total_siniestros and 1 other fieldsHigh correlation
anios_ultimo_siniestro is highly correlated with total_siniestros and 1 other fieldsHigh correlation
n_prod_prev is highly correlated with TipoVehiculo__c and 5 other fieldsHigh correlation
CodigoTipoAsegurado__c is highly correlated with MarcaVehiculo__c and 3 other fieldsHigh correlation
TipoVehiculo__c is highly correlated with n_prod_prev and 5 other fieldsHigh correlation
tipo_poliza_name is highly correlated with TipoVehiculo__c and 5 other fieldsHigh correlation
MarcaVehiculo__c is highly correlated with n_prod_prev and 9 other fieldsHigh correlation
tipo_prod_desc is highly correlated with tipo_poliza_name and 4 other fieldsHigh correlation
end_vig is highly correlated with n_prod_prev and 9 other fieldsHigh correlation
vigencia_dias is highly correlated with n_prod_prev and 9 other fieldsHigh correlation
FechaInicioVigencia__ctrim is highly correlated with MarcaVehiculo__c and 3 other fieldsHigh correlation
churn is highly correlated with n_prod_prev and 4 other fieldsHigh correlation
MdeloVehiculo__c is highly correlated with n_prod_prev and 9 other fieldsHigh correlation
Asegurado__c is highly correlated with n_prod_prev and 1 other fieldsHigh correlation
CodigoTipoAsegurado__c is highly correlated with n_prod_prev and 1 other fieldsHigh correlation
PuntoVenta__c is highly correlated with ClaseVehiculo__c and 1 other fieldsHigh correlation
Producto__c is highly correlated with tipo_poliza_name and 5 other fieldsHigh correlation
tipo_poliza_name is highly correlated with Producto__c and 11 other fieldsHigh correlation
tipo_prod_desc is highly correlated with tipo_poliza_name and 1 other fieldsHigh correlation
ClaseVehiculo__c is highly correlated with PuntoVenta__c and 9 other fieldsHigh correlation
TipoVehiculo__c is highly correlated with PuntoVenta__c and 9 other fieldsHigh correlation
NumeroPoliza__c is highly correlated with Producto__c and 7 other fieldsHigh correlation
FechaInicioVigencia__ctrim is highly correlated with tipo_poliza_name and 3 other fieldsHigh correlation
RamoTecnico__c is highly correlated with tipo_poliza_name and 2 other fieldsHigh correlation
Tipo_poliza_c is highly correlated with tipo_poliza_name and 3 other fieldsHigh correlation
churn is highly correlated with tipo_poliza_name and 4 other fieldsHigh correlation
n_prod_prev is highly correlated with Asegurado__c and 9 other fieldsHigh correlation
total_siniestros is highly correlated with Asegurado__c and 3 other fieldsHigh correlation
total_pagado_smmlv is highly correlated with CodigoTipoAsegurado__c and 9 other fieldsHigh correlation
MarcaVehiculo__c has 14948 (22.2%) missing values Missing
MdeloVehiculo__c has 14948 (22.2%) missing values Missing
end_vig has 54727 (81.5%) missing values Missing
n_prod_prev has 63521 (94.5%) missing values Missing
total_siniestros has 61757 (91.9%) missing values Missing
total_pagado_smmlv has 61757 (91.9%) missing values Missing
anios_ultimo_siniestro has 61757 (91.9%) missing values Missing
total_pagado_smmlv has 736 (1.1%) zeros Zeros

Reproduction

Analysis started2022-05-07 15:28:17.459297
Analysis finished2022-05-07 15:28:37.195610
Duration19.74 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

Asegurado__c
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54851
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14985354.31
Minimum137
Maximum22020206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:37.591231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum137
5-th percentile583868
Q13091077.25
median21371430.5
Q321745721.75
95-th percentile21759782.55
Maximum22020206
Range22020069
Interquartile range (IQR)18654644.5

Descriptive statistics

Standard deviation9205262.486
Coefficient of variation (CV)0.6142839397
Kurtosis-1.453692779
Mean14985354.31
Median Absolute Deviation (MAD)387952.5
Skewness-0.717032356
Sum1.006865956 × 1012
Variance8.473685743 × 1013
MonotonicityNot monotonic
2022-05-07T10:28:37.792593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
583868974
 
1.4%
3556455947
 
1.4%
2839735527
 
0.8%
3750507436
 
0.6%
4022156
 
0.2%
20816593126
 
0.2%
20080990106
 
0.2%
2010713890
 
0.1%
265648572
 
0.1%
85151562
 
0.1%
Other values (54841)63694
94.8%
ValueCountFrequency (%)
1371
 
< 0.1%
2901
 
< 0.1%
4115
< 0.1%
8081
 
< 0.1%
8889
< 0.1%
9127
< 0.1%
96310
< 0.1%
9914
 
< 0.1%
10781
 
< 0.1%
10981
 
< 0.1%
ValueCountFrequency (%)
220202061
 
< 0.1%
218191691
 
< 0.1%
218063031
 
< 0.1%
218051905
< 0.1%
217992271
 
< 0.1%
217910491
 
< 0.1%
217840111
 
< 0.1%
217780875
< 0.1%
217776705
< 0.1%
217776336
< 0.1%

CodigoTipoAsegurado__c
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.0 KiB
1
63481 
4
 
1552
2
 
1352
3
 
805

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters67190
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
163481
94.5%
41552
 
2.3%
21352
 
2.0%
3805
 
1.2%

Length

2022-05-07T10:28:37.938972image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:38.058520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
163481
94.5%
41552
 
2.3%
21352
 
2.0%
3805
 
1.2%

Most occurring characters

ValueCountFrequency (%)
163481
94.5%
41552
 
2.3%
21352
 
2.0%
3805
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number67190
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
163481
94.5%
41552
 
2.3%
21352
 
2.0%
3805
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common67190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
163481
94.5%
41552
 
2.3%
21352
 
2.0%
3805
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII67190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163481
94.5%
41552
 
2.3%
21352
 
2.0%
3805
 
1.2%

PuntoVenta__c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1394
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7373.509689
Minimum1
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:38.177130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile237
Q11689
median9591
Q312254
95-th percentile12836.65
Maximum99999
Range99998
Interquartile range (IQR)10565

Descriptive statistics

Standard deviation5006.555375
Coefficient of variation (CV)0.6789921742
Kurtosis0.05391588932
Mean7373.509689
Median Absolute Deviation (MAD)3047
Skewness-0.1714970568
Sum495426116
Variance25065596.73
MonotonicityNot monotonic
2022-05-07T10:28:38.322992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33012216
 
3.3%
70021883
 
2.8%
121901649
 
2.5%
11491065
 
1.6%
191000
 
1.5%
9721977
 
1.5%
103972
 
1.4%
610966
 
1.4%
1503843
 
1.3%
12254836
 
1.2%
Other values (1384)54783
81.5%
ValueCountFrequency (%)
11
 
< 0.1%
31
 
< 0.1%
5224
0.3%
71
 
< 0.1%
816
 
< 0.1%
95
 
< 0.1%
111
 
< 0.1%
131
 
< 0.1%
1440
 
0.1%
158
 
< 0.1%
ValueCountFrequency (%)
999991
 
< 0.1%
2000111
< 0.1%
130934
 
< 0.1%
130888
< 0.1%
130831
 
< 0.1%
130803
 
< 0.1%
130763
 
< 0.1%
130749
< 0.1%
1307217
< 0.1%
130713
 
< 0.1%

Producto__c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct47
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85773.13701
Minimum1
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:38.474385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q199999
median99999
Q399999
95-th percentile99999
Maximum99999
Range99998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation34924.02539
Coefficient of variation (CV)0.4071674023
Kurtosis2.192955897
Mean85773.13701
Median Absolute Deviation (MAD)0
Skewness-2.04765456
Sum5763097076
Variance1219687549
MonotonicityNot monotonic
2022-05-07T10:28:38.613546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
9999957628
85.8%
171576
 
2.3%
11357
 
2.0%
41182
 
1.8%
99974
 
1.4%
95963
 
1.4%
7581
 
0.9%
21513
 
0.8%
35493
 
0.7%
93359
 
0.5%
Other values (37)1564
 
2.3%
ValueCountFrequency (%)
11357
2.0%
34
 
< 0.1%
41182
1.8%
564
 
0.1%
643
 
0.1%
7581
0.9%
816
 
< 0.1%
925
 
< 0.1%
1029
 
< 0.1%
1120
 
< 0.1%
ValueCountFrequency (%)
9999957628
85.8%
1071
 
< 0.1%
1063
 
< 0.1%
105142
 
0.2%
99974
 
1.4%
9664
 
0.1%
95963
 
1.4%
93359
 
0.5%
8925
 
< 0.1%
8818
 
< 0.1%

tipo_poliza_name
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.0 KiB
s.o.a.t
52242 
individual
 
5165
responsabilidad civil
 
2642
otras
 
2555
de daños tradicional
 
1138
Other values (9)
 
3448

Length

Max length45
Median length7
Mean length8.498050305
Min length5

Characters and Unicode

Total characters570984
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowresponsabilidad civil
2nd rowresponsabilidad civil
3rd rowotras
4th rowotras
5th rowresponsabilidad civil

Common Values

ValueCountFrequency (%)
s.o.a.t52242
77.8%
individual5165
 
7.7%
responsabilidad civil2642
 
3.9%
otras2555
 
3.8%
de daños tradicional1138
 
1.7%
de daños889
 
1.3%
de deudores hipotecarios743
 
1.1%
flotante469
 
0.7%
todo riesgo de obras civiles daños materiales429
 
0.6%
global sector privado412
 
0.6%
Other values (4)506
 
0.8%

Length

2022-05-07T10:28:38.754808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
s.o.a.t52242
66.7%
individual5165
 
6.6%
de3291
 
4.2%
responsabilidad2642
 
3.4%
civil2642
 
3.4%
otras2555
 
3.3%
daños2456
 
3.1%
tradicional1138
 
1.5%
deudores743
 
0.9%
hipotecarios743
 
0.9%
Other values (18)4649
 
5.9%

Most occurring characters

ValueCountFrequency (%)
.156726
27.4%
a74142
13.0%
o67214
11.8%
s66452
11.6%
t59245
 
10.4%
i32320
 
5.7%
d24985
 
4.4%
l14269
 
2.5%
e11260
 
2.0%
11076
 
1.9%
Other values (12)53295
 
9.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter403182
70.6%
Other Punctuation156726
 
27.4%
Space Separator11076
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a74142
18.4%
o67214
16.7%
s66452
16.5%
t59245
14.7%
i32320
8.0%
d24985
 
6.2%
l14269
 
3.5%
e11260
 
2.8%
r10255
 
2.5%
n9829
 
2.4%
Other values (10)33211
8.2%
Other Punctuation
ValueCountFrequency (%)
.156726
100.0%
Space Separator
ValueCountFrequency (%)
11076
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin403182
70.6%
Common167802
29.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a74142
18.4%
o67214
16.7%
s66452
16.5%
t59245
14.7%
i32320
8.0%
d24985
 
6.2%
l14269
 
3.5%
e11260
 
2.8%
r10255
 
2.5%
n9829
 
2.4%
Other values (10)33211
8.2%
Common
ValueCountFrequency (%)
.156726
93.4%
11076
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII568528
99.6%
None2456
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.156726
27.6%
a74142
13.0%
o67214
11.8%
s66452
11.7%
t59245
 
10.4%
i32320
 
5.7%
d24985
 
4.4%
l14269
 
2.5%
e11260
 
2.0%
11076
 
1.9%
Other values (11)50839
 
8.9%
None
ValueCountFrequency (%)
ñ2456
100.0%

tipo_prod_desc
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.0 KiB
otras
64591 
convenios
 
1576
au excepciones
 
505
au ded unic liv
 
493
disp legales
 
25

Length

Max length15
Median length5
Mean length5.237446049
Min length5

Characters and Unicode

Total characters351904
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowotras
2nd rowotras
3rd rowotras
4th rowotras
5th rowotras

Common Values

ValueCountFrequency (%)
otras64591
96.1%
convenios1576
 
2.3%
au excepciones505
 
0.8%
au ded unic liv493
 
0.7%
disp legales25
 
< 0.1%

Length

2022-05-07T10:28:38.875922image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:38.996141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
otras64591
93.3%
convenios1576
 
2.3%
au998
 
1.4%
excepciones505
 
0.7%
ded493
 
0.7%
unic493
 
0.7%
liv493
 
0.7%
disp25
 
< 0.1%
legales25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o68248
19.4%
s66722
19.0%
a65614
18.6%
r64591
18.4%
t64591
18.4%
n4150
 
1.2%
e3634
 
1.0%
i3092
 
0.9%
c3079
 
0.9%
v2069
 
0.6%
Other values (7)6114
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter349895
99.4%
Space Separator2009
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o68248
19.5%
s66722
19.1%
a65614
18.8%
r64591
18.5%
t64591
18.5%
n4150
 
1.2%
e3634
 
1.0%
i3092
 
0.9%
c3079
 
0.9%
v2069
 
0.6%
Other values (6)4105
 
1.2%
Space Separator
ValueCountFrequency (%)
2009
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin349895
99.4%
Common2009
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o68248
19.5%
s66722
19.1%
a65614
18.8%
r64591
18.5%
t64591
18.5%
n4150
 
1.2%
e3634
 
1.0%
i3092
 
0.9%
c3079
 
0.9%
v2069
 
0.6%
Other values (6)4105
 
1.2%
Common
ValueCountFrequency (%)
2009
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII351904
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o68248
19.4%
s66722
19.0%
a65614
18.6%
r64591
18.4%
t64591
18.4%
n4150
 
1.2%
e3634
 
1.0%
i3092
 
0.9%
c3079
 
0.9%
v2069
 
0.6%
Other values (7)6114
 
1.7%

ClaseVehiculo__c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22248.17302
Minimum1
Maximum99999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:39.095443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q35
95-th percentile99999
Maximum99999
Range99998
Interquartile range (IQR)4

Descriptive statistics

Standard deviation41590.09345
Coefficient of variation (CV)1.86937118
Kurtosis-0.2188813508
Mean22248.17302
Median Absolute Deviation (MAD)0
Skewness1.334588012
Sum1494854745
Variance1729735873
MonotonicityNot monotonic
2022-05-07T10:28:39.198014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
146755
69.6%
9999914948
 
22.2%
52934
 
4.4%
21426
 
2.1%
3527
 
0.8%
7214
 
0.3%
6196
 
0.3%
4105
 
0.2%
961
 
0.1%
824
 
< 0.1%
ValueCountFrequency (%)
146755
69.6%
21426
 
2.1%
3527
 
0.8%
4105
 
0.2%
52934
 
4.4%
6196
 
0.3%
7214
 
0.3%
824
 
< 0.1%
961
 
0.1%
9999914948
 
22.2%
ValueCountFrequency (%)
9999914948
 
22.2%
961
 
0.1%
824
 
< 0.1%
7214
 
0.3%
6196
 
0.3%
52934
 
4.4%
4105
 
0.2%
3527
 
0.8%
21426
 
2.1%
146755
69.6%

MarcaVehiculo__c
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing14948
Missing (%)22.2%
Memory size525.0 KiB
97.0
52242 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters208968
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row97.0
2nd row97.0
3rd row97.0
4th row97.0
5th row97.0

Common Values

ValueCountFrequency (%)
97.052242
77.8%
(Missing)14948
 
22.2%

Length

2022-05-07T10:28:39.307089image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:39.408809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
97.052242
100.0%

Most occurring characters

ValueCountFrequency (%)
952242
25.0%
752242
25.0%
.52242
25.0%
052242
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number156726
75.0%
Other Punctuation52242
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
952242
33.3%
752242
33.3%
052242
33.3%
Other Punctuation
ValueCountFrequency (%)
.52242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common208968
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
952242
25.0%
752242
25.0%
.52242
25.0%
052242
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII208968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
952242
25.0%
752242
25.0%
.52242
25.0%
052242
25.0%

MdeloVehiculo__c
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing14948
Missing (%)22.2%
Memory size525.0 KiB
999.0
52242 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters261210
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row999.0
2nd row999.0
3rd row999.0
4th row999.0
5th row999.0

Common Values

ValueCountFrequency (%)
999.052242
77.8%
(Missing)14948
 
22.2%

Length

2022-05-07T10:28:39.496539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:39.598308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
999.052242
100.0%

Most occurring characters

ValueCountFrequency (%)
9156726
60.0%
.52242
 
20.0%
052242
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number208968
80.0%
Other Punctuation52242
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9156726
75.0%
052242
 
25.0%
Other Punctuation
ValueCountFrequency (%)
.52242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common261210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
9156726
60.0%
.52242
 
20.0%
052242
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII261210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9156726
60.0%
.52242
 
20.0%
052242
 
20.0%

TipoVehiculo__c
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.0 KiB
0
52242 
99999
14948 

Length

Max length5
Median length1
Mean length1.88989433
Min length1

Characters and Unicode

Total characters126982
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99999
2nd row99999
3rd row99999
4th row99999
5th row99999

Common Values

ValueCountFrequency (%)
052242
77.8%
9999914948
 
22.2%

Length

2022-05-07T10:28:39.694684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:39.820333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
052242
77.8%
9999914948
 
22.2%

Most occurring characters

ValueCountFrequency (%)
974740
58.9%
052242
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number126982
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
974740
58.9%
052242
41.1%

Most occurring scripts

ValueCountFrequency (%)
Common126982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
974740
58.9%
052242
41.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII126982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
974740
58.9%
052242
41.1%

NumeroPoliza__c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct60441
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3884922.48
Minimum1000002
Maximum4845222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:39.939913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1000002
5-th percentile1002701
Q14098084.25
median4585393
Q34617661.75
95-th percentile4631210.55
Maximum4845222
Range3845220
Interquartile range (IQR)519577.5

Descriptive statistics

Standard deviation1212762.246
Coefficient of variation (CV)0.3121715432
Kurtosis1.330058942
Mean3884922.48
Median Absolute Deviation (MAD)47225.5
Skewness-1.696237083
Sum2.610279414 × 1011
Variance1.470792265 × 1012
MonotonicityNot monotonic
2022-05-07T10:28:40.093912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100118216
 
< 0.1%
100048914
 
< 0.1%
100121414
 
< 0.1%
100117614
 
< 0.1%
100426113
 
< 0.1%
100117913
 
< 0.1%
100110213
 
< 0.1%
100425913
 
< 0.1%
100110612
 
< 0.1%
100028612
 
< 0.1%
Other values (60431)67056
99.8%
ValueCountFrequency (%)
10000027
< 0.1%
10000047
< 0.1%
10000063
< 0.1%
10000071
 
< 0.1%
10000095
< 0.1%
10000105
< 0.1%
10000131
 
< 0.1%
10000144
< 0.1%
10000154
< 0.1%
10000165
< 0.1%
ValueCountFrequency (%)
48452221
< 0.1%
46634191
< 0.1%
46613421
< 0.1%
46494621
< 0.1%
46494061
< 0.1%
46457191
< 0.1%
46457181
< 0.1%
46405931
< 0.1%
46347891
< 0.1%
46347881
< 0.1%

FechaInicioVigencia__ctrim
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.0 KiB
02-2021
63492 
01-2021
 
3698

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters470330
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row02-2021
2nd row02-2021
3rd row02-2021
4th row02-2021
5th row01-2021

Common Values

ValueCountFrequency (%)
02-202163492
94.5%
01-20213698
 
5.5%

Length

2022-05-07T10:28:40.246160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:40.363317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
02-202163492
94.5%
01-20213698
 
5.5%

Most occurring characters

ValueCountFrequency (%)
2197872
42.1%
0134380
28.6%
170888
 
15.1%
-67190
 
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number403140
85.7%
Dash Punctuation67190
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2197872
49.1%
0134380
33.3%
170888
 
17.6%
Dash Punctuation
ValueCountFrequency (%)
-67190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common470330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2197872
42.1%
0134380
28.6%
170888
 
15.1%
-67190
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII470330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2197872
42.1%
0134380
28.6%
170888
 
15.1%
-67190
 
14.3%

vigencia_dias
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.0 KiB
365
67190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters201570
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row365
2nd row365
3rd row365
4th row365
5th row365

Common Values

ValueCountFrequency (%)
36567190
100.0%

Length

2022-05-07T10:28:40.463354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:40.576342image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
36567190
100.0%

Most occurring characters

ValueCountFrequency (%)
367190
33.3%
667190
33.3%
567190
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number201570
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
367190
33.3%
667190
33.3%
567190
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common201570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
367190
33.3%
667190
33.3%
567190
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII201570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
367190
33.3%
667190
33.3%
567190
33.3%

RamoTecnico__c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.751212978
Minimum1
Maximum84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:40.663553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q18
median8
Q38
95-th percentile13
Maximum84
Range83
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.412045375
Coefficient of variation (CV)0.8469734874
Kurtosis89.09285772
Mean8.751212978
Median Absolute Deviation (MAD)0
Skewness9.230088168
Sum587994
Variance54.93841665
MonotonicityNot monotonic
2022-05-07T10:28:40.784574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
852242
77.8%
75323
 
7.9%
133738
 
5.6%
31602
 
2.4%
111595
 
2.4%
4790
 
1.2%
1728
 
1.1%
83288
 
0.4%
81212
 
0.3%
5145
 
0.2%
Other values (10)527
 
0.8%
ValueCountFrequency (%)
1728
 
1.1%
293
 
0.1%
31602
 
2.4%
4790
 
1.2%
5145
 
0.2%
628
 
< 0.1%
75323
 
7.9%
852242
77.8%
111595
 
2.4%
133738
 
5.6%
ValueCountFrequency (%)
84129
0.2%
83288
0.4%
81212
0.3%
291
 
< 0.1%
233
 
< 0.1%
19130
0.2%
1816
 
< 0.1%
17107
 
0.2%
1518
 
< 0.1%
142
 
< 0.1%

Tipo_poliza_c
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.289090638
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:40.890384image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile4
Maximum14
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.156473853
Coefficient of variation (CV)0.89712377
Kurtosis22.53680559
Mean1.289090638
Median Absolute Deviation (MAD)0
Skewness4.615878672
Sum86614
Variance1.337431774
MonotonicityNot monotonic
2022-05-07T10:28:41.006102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
162245
92.6%
43107
 
4.6%
81287
 
1.9%
2279
 
0.4%
3225
 
0.3%
1126
 
< 0.1%
517
 
< 0.1%
142
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
162245
92.6%
2279
 
0.4%
3225
 
0.3%
43107
 
4.6%
517
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81287
 
1.9%
1126
 
< 0.1%
142
 
< 0.1%
ValueCountFrequency (%)
142
 
< 0.1%
1126
 
< 0.1%
81287
 
1.9%
71
 
< 0.1%
61
 
< 0.1%
517
 
< 0.1%
43107
 
4.6%
3225
 
0.3%
2279
 
0.4%
162245
92.6%

end_vig
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing54727
Missing (%)81.5%
Memory size525.0 KiB
365.0
12463 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters62315
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row365.0
2nd row365.0
3rd row365.0
4th row365.0
5th row365.0

Common Values

ValueCountFrequency (%)
365.012463
 
18.5%
(Missing)54727
81.5%

Length

2022-05-07T10:28:41.123214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:41.229606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
365.012463
100.0%

Most occurring characters

ValueCountFrequency (%)
312463
20.0%
612463
20.0%
512463
20.0%
.12463
20.0%
012463
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number49852
80.0%
Other Punctuation12463
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
312463
25.0%
612463
25.0%
512463
25.0%
012463
25.0%
Other Punctuation
ValueCountFrequency (%)
.12463
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common62315
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
312463
20.0%
612463
20.0%
512463
20.0%
.12463
20.0%
012463
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII62315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
312463
20.0%
612463
20.0%
512463
20.0%
.12463
20.0%
012463
20.0%

churn
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.7 KiB
True
54727 
False
12463 
ValueCountFrequency (%)
True54727
81.5%
False12463
 
18.5%
2022-05-07T10:28:41.323380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

n_prod_prev
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct5
Distinct (%)0.1%
Missing63521
Missing (%)94.5%
Memory size525.0 KiB
1.0
1580 
3.0
1038 
8.0
947 
2.0
 
94
4.0
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters11007
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.01580
 
2.4%
3.01038
 
1.5%
8.0947
 
1.4%
2.094
 
0.1%
4.010
 
< 0.1%
(Missing)63521
94.5%

Length

2022-05-07T10:28:41.417188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-07T10:28:41.552492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.01580
43.1%
3.01038
28.3%
8.0947
25.8%
2.094
 
2.6%
4.010
 
0.3%

Most occurring characters

ValueCountFrequency (%)
.3669
33.3%
03669
33.3%
11580
14.4%
31038
 
9.4%
8947
 
8.6%
294
 
0.9%
410
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number7338
66.7%
Other Punctuation3669
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
03669
50.0%
11580
21.5%
31038
 
14.1%
8947
 
12.9%
294
 
1.3%
410
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.3669
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11007
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.3669
33.3%
03669
33.3%
11580
14.4%
31038
 
9.4%
8947
 
8.6%
294
 
0.9%
410
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11007
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.3669
33.3%
03669
33.3%
11580
14.4%
31038
 
9.4%
8947
 
8.6%
294
 
0.9%
410
 
0.1%

total_siniestros
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct32
Distinct (%)0.6%
Missing61757
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean42.02963372
Minimum1
Maximum940
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:41.670515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median19
Q338
95-th percentile150
Maximum940
Range939
Interquartile range (IQR)35

Descriptive statistics

Standard deviation56.77385219
Coefficient of variation (CV)1.350805305
Kurtosis34.04193112
Mean42.02963372
Median Absolute Deviation (MAD)18
Skewness3.281117197
Sum228347
Variance3223.270293
MonotonicityNot monotonic
2022-05-07T10:28:41.797158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
150974
 
1.4%
38948
 
1.4%
1896
 
1.3%
37569
 
0.8%
16481
 
0.7%
2273
 
0.4%
3266
 
0.4%
4225
 
0.3%
15114
 
0.2%
19106
 
0.2%
Other values (22)581
 
0.9%
(Missing)61757
91.9%
ValueCountFrequency (%)
1896
1.3%
2273
 
0.4%
3266
 
0.4%
4225
 
0.3%
598
 
0.1%
640
 
0.1%
781
 
0.1%
8100
 
0.1%
933
 
< 0.1%
1052
 
0.1%
ValueCountFrequency (%)
9403
 
< 0.1%
150974
1.4%
902
 
< 0.1%
805
 
< 0.1%
529
 
< 0.1%
511
 
< 0.1%
456
 
< 0.1%
38948
1.4%
37569
0.8%
362
 
< 0.1%

total_pagado_smmlv
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct386
Distinct (%)7.1%
Missing61757
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean2327.327717
Minimum0
Maximum55871.95629
Zeros736
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:41.936321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130.44892846
median254.8747852
Q33065.269972
95-th percentile8833.286309
Maximum55871.95629
Range55871.95629
Interquartile range (IQR)3034.821043

Descriptive statistics

Standard deviation3439.853407
Coefficient of variation (CV)1.478027088
Kurtosis21.66327377
Mean2327.327717
Median Absolute Deviation (MAD)254.8747852
Skewness2.555526255
Sum12644371.48
Variance11832591.46
MonotonicityNot monotonic
2022-05-07T10:28:42.069673image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8833.286309974
 
1.4%
3065.269972947
 
1.4%
0736
 
1.1%
1306.925184527
 
0.8%
57.0596277436
 
0.6%
38.70535986156
 
0.2%
17.35001589126
 
0.2%
207.8908871106
 
0.2%
50.8582310990
 
0.1%
370.254176672
 
0.1%
Other values (376)1263
 
1.9%
(Missing)61757
91.9%
ValueCountFrequency (%)
0736
1.1%
0.16790493611
 
< 0.1%
0.2254200762
 
< 0.1%
0.24393578171
 
< 0.1%
0.25577693981
 
< 0.1%
0.28442774341
 
< 0.1%
0.29319799321
 
< 0.1%
0.30737480271
 
< 0.1%
0.30774022981
 
< 0.1%
0.3333311321
 
< 0.1%
ValueCountFrequency (%)
55871.956292
 
< 0.1%
22272.95173
 
< 0.1%
8833.286309974
1.4%
4385.6987732
 
< 0.1%
3065.269972947
1.4%
2345.7519035
 
< 0.1%
2265.8611021
 
< 0.1%
1556.7514481
 
< 0.1%
1446.6289351
 
< 0.1%
1306.925184527
0.8%

anios_ultimo_siniestro
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct221
Distinct (%)4.1%
Missing61757
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean0.2478662864
Minimum0.002739726027
Maximum9.465753425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.0 KiB
2022-05-07T10:28:42.240668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.002739726027
5-th percentile0.002739726027
Q10.005479452055
median0.008219178082
Q30.07671232877
95-th percentile1.452054795
Maximum9.465753425
Range9.463013699
Interquartile range (IQR)0.07123287671

Descriptive statistics

Standard deviation0.9094300083
Coefficient of variation (CV)3.66903471
Kurtosis38.02820082
Mean0.2478662864
Median Absolute Deviation (MAD)0.005479452055
Skewness5.773418116
Sum1346.657534
Variance0.82706294
MonotonicityNot monotonic
2022-05-07T10:28:42.385795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0082191780821094
 
1.6%
0.002739726027991
 
1.5%
0.005479452055742
 
1.1%
0.01095890411563
 
0.8%
0.1095890411182
 
0.3%
0.09315068493138
 
0.2%
0.0712328767187
 
0.1%
0.0767123287768
 
0.1%
0.0164383561658
 
0.1%
0.0246575342556
 
0.1%
Other values (211)1454
 
2.2%
(Missing)61757
91.9%
ValueCountFrequency (%)
0.002739726027991
1.5%
0.005479452055742
1.1%
0.0082191780821094
1.6%
0.01095890411563
0.8%
0.0136986301439
 
0.1%
0.0164383561658
 
0.1%
0.0219178082215
 
< 0.1%
0.0246575342556
 
0.1%
0.027397260277
 
< 0.1%
0.030136986325
 
< 0.1%
ValueCountFrequency (%)
9.4657534251
 
< 0.1%
8.758904117
< 0.1%
87
< 0.1%
7.5315068492
 
< 0.1%
7.1780821926
< 0.1%
7.1013698633
 
< 0.1%
7.0054794523
 
< 0.1%
6.9945205481
 
< 0.1%
6.23561643812
< 0.1%
6.0082191787
< 0.1%

Interactions

2022-05-07T10:28:34.384945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:21.866059image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:23.452802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:24.866685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:26.207592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:27.619356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:28.961930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:30.563207image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:31.927796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:33.156360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:34.501462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:22.018404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:23.582626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:24.994179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:26.338168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:27.758783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:29.095968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:30.693188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:32.043180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:33.274676image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:34.627233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:22.151962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:23.709456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:25.132313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:26.470780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:27.892809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:29.224404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:30.829271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:32.174974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:33.401626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:34.750437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:22.277141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:23.851060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:25.271024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:26.602624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:28.022725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:29.355034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:30.967118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:32.294463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:33.522087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:34.867002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:22.413346image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:24.016634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:25.409351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:26.733085image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:28.156854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:29.496969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:31.123349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:32.432402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:33.645927image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:34.992190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:22.560526image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:24.160290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:25.566952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:26.877822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:28.305456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:29.658194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:31.265656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:32.563970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:33.781638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:35.104654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:22.697636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:24.288660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:25.702843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:27.018197image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:28.433031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:29.782114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:31.388206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:32.676426image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:33.897943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:35.231752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:22.834131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:24.418391image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:25.828778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:27.188196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:28.569782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:29.912524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:31.516945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:32.800824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:34.027193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:35.348720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:23.200894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:24.582871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:25.948437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:27.351908image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:28.693792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:30.273999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:31.647793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:32.918954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:34.148901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:35.468043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:23.326650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:24.733780image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:26.072628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:27.490075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:28.820417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:30.396322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:31.802148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:33.038398image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-07T10:28:34.270732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-07T10:28:42.766606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-07T10:28:43.018459image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-07T10:28:43.263184image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-07T10:28:43.492030image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-07T10:28:43.674395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-07T10:28:35.719660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-07T10:28:36.238830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-07T10:28:36.688693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-07T10:28:36.979008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Asegurado__cCodigoTipoAsegurado__cPuntoVenta__cProducto__ctipo_poliza_nametipo_prod_descClaseVehiculo__cMarcaVehiculo__cMdeloVehiculo__cTipoVehiculo__cNumeroPoliza__cFechaInicioVigencia__ctrimvigencia_diasRamoTecnico__cTipo_poliza_cend_vigchurnn_prod_prevtotal_siniestrostotal_pagado_smmlvanios_ultimo_siniestro
0715728270024responsabilidad civilotras99999NaNNaN99999100850102-2021365131365.0False1.036.0517.1278990.005479
1715728270024responsabilidad civilotras99999NaNNaN99999100848902-2021365131365.0False1.036.0517.1278990.005479
235141320299999otrasotras99999NaNNaN99999100114302-2021365151NaNTrueNaNNaNNaNNaN
32497371320299999otrasotras99999NaNNaN99999100114402-2021365151NaNTrueNaNNaNNaNNaN
420043213132021responsabilidad civilotras99999NaNNaN99999106015101-2021365131NaNTrue2.01.0254.5882240.005479
520005898132021responsabilidad civilotras99999NaNNaN99999106015201-2021365131NaNTrue1.01.00.0000000.394521
63511668132021responsabilidad civilotras99999NaNNaN99999106015301-2021365131NaNTrue2.01.00.0000000.063014
720492197132021responsabilidad civilotras99999NaNNaN99999106015401-2021365131NaNTrue2.0NaNNaNNaN
82468858132021responsabilidad civilotras99999NaNNaN99999106015501-2021365131NaNTrue2.01.04385.6987731.841096
9204921971320231responsabilidad civilotras99999NaNNaN99999106018701-2021365131365.0False2.0NaNNaNNaN

Last rows

Asegurado__cCodigoTipoAsegurado__cPuntoVenta__cProducto__ctipo_poliza_nametipo_prod_descClaseVehiculo__cMarcaVehiculo__cMdeloVehiculo__cTipoVehiculo__cNumeroPoliza__cFechaInicioVigencia__ctrimvigencia_diasRamoTecnico__cTipo_poliza_cend_vigchurnn_prod_prevtotal_siniestrostotal_pagado_smmlvanios_ultimo_siniestro
671802225121330199999todo riesgo de obras civiles daños materialesotras99999NaNNaN99999100471101-202136538365.0FalseNaNNaNNaNNaN
671812225121330199999otrasotras99999NaNNaN99999100471101-2021365118365.0FalseNaNNaNNaNNaN
67182222512133011otrasotras99999NaNNaN99999100471101-2021365138365.0FalseNaNNaNNaNNaN
671835838681972199individualotras99999NaNNaN99999317305602-202136571NaNTrue3.0150.08833.2863090.00274
671844594791320117individualconvenios99999NaNNaN99999317305901-202136571365.0FalseNaNNaNNaNNaN
67185198229811999999s.o.a.totras297.0999.00457382201-202136581365.0FalseNaNNaNNaNNaN
6718618760611320217individualconvenios99999NaNNaN99999312970001-202136571NaNTrueNaNNaNNaNNaN
6718720025245140413colectivaotras99999NaNNaN99999303437601-202136572NaNTrue1.0NaNNaNNaN
6718811359001182095individualotras99999NaNNaN99999307590701-202136571NaNTrueNaNNaNNaNNaN
67189207132841330399999s.o.a.totras197.0999.00459516202-202136581NaNTrueNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

Asegurado__cCodigoTipoAsegurado__cPuntoVenta__cProducto__ctipo_poliza_nametipo_prod_descClaseVehiculo__cMarcaVehiculo__cMdeloVehiculo__cTipoVehiculo__cNumeroPoliza__cFechaInicioVigencia__ctrimvigencia_diasRamoTecnico__cTipo_poliza_cend_vigchurnn_prod_prevtotal_siniestrostotal_pagado_smmlvanios_ultimo_siniestro# duplicates
019232700299999s.o.a.totras297.0999.00409955001-202136581365.0False1.07.028.3614400.0109592
125984700299999s.o.a.totras397.0999.00409857602-202136581365.0False1.07.0222.7952440.1068492
225984700299999s.o.a.totras397.0999.00409857702-202136581365.0False1.07.0222.7952440.1068492
34022230199999s.o.a.totras197.0999.00415085802-202136581365.0False1.04.038.7053600.1095892
44022230199999s.o.a.totras197.0999.00415085902-202136581365.0False1.04.038.7053600.1095892
54022230199999s.o.a.totras197.0999.00415086002-202136581365.0False1.04.038.7053600.1095892
64022230199999s.o.a.totras197.0999.00415086102-202136581365.0False1.04.038.7053600.1095892
74022230199999s.o.a.totras197.0999.00415086202-202136581365.0False1.04.038.7053600.1095892
84022230199999s.o.a.totras197.0999.00415086302-202136581365.0False1.04.038.7053600.1095892
94022230199999s.o.a.totras197.0999.00415086402-202136581365.0False1.04.038.7053600.1095892